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Record W3111363672 · doi:10.3390/nu12123803

Piece of Cake: Coping with COVID-19

2020· article· en· W3111363672 on OpenAlexaffabout
Melissa J. Chee, Nikita K. Koziel Ly, Hymie Anisman, Kimberly Matheson

Bibliographic record

VenueNutrients · 2020
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsRoyal Ottawa Mental Health CentreCarleton University
Fundersnot available
KeywordsCoping (psychology)PandemicCoronavirus disease 2019 (COVID-19)PsychologyAffect (linguistics)StressorEnvironmental health2019-20 coronavirus outbreakMedicineGerontologySocial psychologyClinical psychologyDiseaseInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

To limit the spread of coronavirus disease 2019 (COVID-19), many individuals were instructed to stay at home, and teleworking became commonplace. Meanwhile, many others were laid off or worked reduced hours, and some front line workers were required to work longer hours. Concurrently, a surge in reports of “pandemic baking” suggested a cascade effect on eating behaviors, which may be an inadvertent strategy to cope with stress. We conducted an online survey of people living in Canada or the United States (N = 680) to assess how employment change may have been experienced as stressful and linked to a shift in food choices. Regression models suggested that reduced hours and being laid off were associated with greater stress appraisals, avoidant- and emotion-focused coping responses, and negative affect. In turn, negative affect was associated with eating to cope and unhealthy snack choices, like salty or sweet treats. Our study emphasizes that under stressful conditions, such as those experienced during the COVID-19 pandemic, some coping strategies may contribute to the greater vulnerability to downstream effects, particularly those relating to eating choices and nutritional balances.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.117
GPT teacher head0.426
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2020
Admission routes2
Has abstractyes

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